Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, C

Percentage Accurate: 99.9% → 100.0%

Time: 5.3s

Alternatives: 10

Speedup: 1.0×

• 20.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot \left(y + z\right) + z \cdot 5 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x (+ y z)) (* z 5.0)))

C

double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x * (y + z)) + (z * 5.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Python

def code(x, y, z):
	return (x * (y + z)) + (z * 5.0)

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(y + z)) + Float64(z * 5.0))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (y + z)) + (z * 5.0);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] + N[(z * 5.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(y + z\right) + z \cdot 5 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x (+ y z)) (* z 5.0)))

C

double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x * (y + z)) + (z * 5.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Python

def code(x, y, z):
	return (x * (y + z)) + (z * 5.0)

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(y + z)) + Float64(z * 5.0))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (y + z)) + (z * 5.0);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] + N[(z * 5.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(z, 5, x \cdot \left(z + y\right)\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma z 5.0 (* x (+ z y))))

C

double code(double x, double y, double z) {
	return fma(z, 5.0, (x * (z + y)));
}

Julia

function code(x, y, z)
	return fma(z, 5.0, Float64(x * Float64(z + y)))
end

Wolfram

code[x_, y_, z_] := N[(z * 5.0 + N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]
2. Step-by-step derivation

   1. +-commutative 99.9%

      \[\leadsto \color{blue}{z \cdot 5 + x \cdot \left(y + z\right)} \]

   2. fma-def 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 5, x \cdot \left(y + z\right)\right)} \]

3. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(z, 5, x \cdot \left(y + z\right)\right)} \]

4. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(z, 5, x \cdot \left(z + y\right)\right) \]

Alternative 2: 84.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z + y\right)\\ t_1 := z \cdot \left(5 + x\right)\\ \mathbf{if}\;x \leq -10500000:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -4.15 \cdot 10^{-51}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.15 \cdot 10^{-84}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 3.6 \cdot 10^{-35}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (+ z y))) (t_1 (* z (+ 5.0 x))))
   (if (<= x -10500000.0)
     t_0
     (if (<= x -4.15e-51)
       t_1
       (if (<= x -1.15e-84) (* x y) (if (<= x 3.6e-35) t_1 t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z + y);
	double t_1 = z * (5.0 + x);
	double tmp;
	if (x <= -10500000.0) {
		tmp = t_0;
	} else if (x <= -4.15e-51) {
		tmp = t_1;
	} else if (x <= -1.15e-84) {
		tmp = x * y;
	} else if (x <= 3.6e-35) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = x * (z + y)
    t_1 = z * (5.0d0 + x)
    if (x <= (-10500000.0d0)) then
        tmp = t_0
    else if (x <= (-4.15d-51)) then
        tmp = t_1
    else if (x <= (-1.15d-84)) then
        tmp = x * y
    else if (x <= 3.6d-35) then
        tmp = t_1
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x * (z + y);
	double t_1 = z * (5.0 + x);
	double tmp;
	if (x <= -10500000.0) {
		tmp = t_0;
	} else if (x <= -4.15e-51) {
		tmp = t_1;
	} else if (x <= -1.15e-84) {
		tmp = x * y;
	} else if (x <= 3.6e-35) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z + y)
	t_1 = z * (5.0 + x)
	tmp = 0
	if x <= -10500000.0:
		tmp = t_0
	elif x <= -4.15e-51:
		tmp = t_1
	elif x <= -1.15e-84:
		tmp = x * y
	elif x <= 3.6e-35:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z + y))
	t_1 = Float64(z * Float64(5.0 + x))
	tmp = 0.0
	if (x <= -10500000.0)
		tmp = t_0;
	elseif (x <= -4.15e-51)
		tmp = t_1;
	elseif (x <= -1.15e-84)
		tmp = Float64(x * y);
	elseif (x <= 3.6e-35)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z + y);
	t_1 = z * (5.0 + x);
	tmp = 0.0;
	if (x <= -10500000.0)
		tmp = t_0;
	elseif (x <= -4.15e-51)
		tmp = t_1;
	elseif (x <= -1.15e-84)
		tmp = x * y;
	elseif (x <= 3.6e-35)
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(z * N[(5.0 + x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -10500000.0], t$95$0, If[LessEqual[x, -4.15e-51], t$95$1, If[LessEqual[x, -1.15e-84], N[(x * y), $MachinePrecision], If[LessEqual[x, 3.6e-35], t$95$1, t$95$0]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.05e7 or 3.60000000000000019e-35 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 99.4%

      \[\leadsto \color{blue}{\left(y + z\right) \cdot x} \]
   3. Step-by-step derivation

      1. +-commutative 99.4%

         \[\leadsto \color{blue}{\left(z + y\right)} \cdot x \]

   4. Simplified 99.4%

      \[\leadsto \color{blue}{\left(z + y\right) \cdot x} \]

   if -1.05e7 < x < -4.14999999999999988e-51 or -1.1499999999999999e-84 < x < 3.60000000000000019e-35

   1. Initial program 99.8%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around 0 72.8%

      \[\leadsto \color{blue}{z \cdot x + 5 \cdot z} \]
   3. Step-by-step derivation

      1. +-commutative 72.8%

         \[\leadsto \color{blue}{5 \cdot z + z \cdot x} \]

      2. *-commutative 72.8%

         \[\leadsto 5 \cdot z + \color{blue}{x \cdot z} \]

      3. distribute-rgt-in 72.8%

         \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   4. Simplified 72.8%

      \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   if -4.14999999999999988e-51 < x < -1.1499999999999999e-84

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 100.0%

      \[\leadsto \color{blue}{y \cdot x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 85.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -10500000:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \mathbf{elif}\;x \leq -4.15 \cdot 10^{-51}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{elif}\;x \leq -1.15 \cdot 10^{-84}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 3.6 \cdot 10^{-35}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \end{array} \]

Alternative 3: 83.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \left(5 + x\right)\\ \mathbf{if}\;x \leq -10500000:\\ \;\;\;\;z \cdot x + x \cdot y\\ \mathbf{elif}\;x \leq -5.6 \cdot 10^{-53}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -2.5 \cdot 10^{-81}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 1.95 \cdot 10^{-32}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (+ 5.0 x))))
   (if (<= x -10500000.0)
     (+ (* z x) (* x y))
     (if (<= x -5.6e-53)
       t_0
       (if (<= x -2.5e-81) (* x y) (if (<= x 1.95e-32) t_0 (* x (+ z y))))))))

C

double code(double x, double y, double z) {
	double t_0 = z * (5.0 + x);
	double tmp;
	if (x <= -10500000.0) {
		tmp = (z * x) + (x * y);
	} else if (x <= -5.6e-53) {
		tmp = t_0;
	} else if (x <= -2.5e-81) {
		tmp = x * y;
	} else if (x <= 1.95e-32) {
		tmp = t_0;
	} else {
		tmp = x * (z + y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = z * (5.0d0 + x)
    if (x <= (-10500000.0d0)) then
        tmp = (z * x) + (x * y)
    else if (x <= (-5.6d-53)) then
        tmp = t_0
    else if (x <= (-2.5d-81)) then
        tmp = x * y
    else if (x <= 1.95d-32) then
        tmp = t_0
    else
        tmp = x * (z + y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z * (5.0 + x);
	double tmp;
	if (x <= -10500000.0) {
		tmp = (z * x) + (x * y);
	} else if (x <= -5.6e-53) {
		tmp = t_0;
	} else if (x <= -2.5e-81) {
		tmp = x * y;
	} else if (x <= 1.95e-32) {
		tmp = t_0;
	} else {
		tmp = x * (z + y);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * (5.0 + x)
	tmp = 0
	if x <= -10500000.0:
		tmp = (z * x) + (x * y)
	elif x <= -5.6e-53:
		tmp = t_0
	elif x <= -2.5e-81:
		tmp = x * y
	elif x <= 1.95e-32:
		tmp = t_0
	else:
		tmp = x * (z + y)
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * Float64(5.0 + x))
	tmp = 0.0
	if (x <= -10500000.0)
		tmp = Float64(Float64(z * x) + Float64(x * y));
	elseif (x <= -5.6e-53)
		tmp = t_0;
	elseif (x <= -2.5e-81)
		tmp = Float64(x * y);
	elseif (x <= 1.95e-32)
		tmp = t_0;
	else
		tmp = Float64(x * Float64(z + y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * (5.0 + x);
	tmp = 0.0;
	if (x <= -10500000.0)
		tmp = (z * x) + (x * y);
	elseif (x <= -5.6e-53)
		tmp = t_0;
	elseif (x <= -2.5e-81)
		tmp = x * y;
	elseif (x <= 1.95e-32)
		tmp = t_0;
	else
		tmp = x * (z + y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[(5.0 + x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -10500000.0], N[(N[(z * x), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -5.6e-53], t$95$0, If[LessEqual[x, -2.5e-81], N[(x * y), $MachinePrecision], If[LessEqual[x, 1.95e-32], t$95$0, N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.05e7

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 99.9%

      \[\leadsto \color{blue}{\left(y + z\right) \cdot x} \]
   3. Step-by-step derivation

      1. +-commutative 99.9%

         \[\leadsto \color{blue}{\left(z + y\right)} \cdot x \]

   4. Simplified 99.9%

      \[\leadsto \color{blue}{\left(z + y\right) \cdot x} \]

   5. Taylor expanded in z around 0 100.0%

      \[\leadsto \color{blue}{z \cdot x + y \cdot x} \]

   if -1.05e7 < x < -5.59999999999999971e-53 or -2.4999999999999999e-81 < x < 1.9500000000000001e-32

   1. Initial program 99.8%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around 0 72.8%

      \[\leadsto \color{blue}{z \cdot x + 5 \cdot z} \]
   3. Step-by-step derivation

      1. +-commutative 72.8%

         \[\leadsto \color{blue}{5 \cdot z + z \cdot x} \]

      2. *-commutative 72.8%

         \[\leadsto 5 \cdot z + \color{blue}{x \cdot z} \]

      3. distribute-rgt-in 72.8%

         \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   4. Simplified 72.8%

      \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   if -5.59999999999999971e-53 < x < -2.4999999999999999e-81

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 100.0%

      \[\leadsto \color{blue}{y \cdot x} \]

   if 1.9500000000000001e-32 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 98.8%

      \[\leadsto \color{blue}{\left(y + z\right) \cdot x} \]
   3. Step-by-step derivation

      1. +-commutative 98.8%

         \[\leadsto \color{blue}{\left(z + y\right)} \cdot x \]

   4. Simplified 98.8%

      \[\leadsto \color{blue}{\left(z + y\right) \cdot x} \]
3. Recombined 4 regimes into one program.

4. Final simplification 85.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -10500000:\\ \;\;\;\;z \cdot x + x \cdot y\\ \mathbf{elif}\;x \leq -5.6 \cdot 10^{-53}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{elif}\;x \leq -2.5 \cdot 10^{-81}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 1.95 \cdot 10^{-32}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \end{array} \]

Alternative 4: 83.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -10500000:\\ \;\;\;\;z \cdot x + x \cdot y\\ \mathbf{elif}\;x \leq -1.56 \cdot 10^{-52}:\\ \;\;\;\;z \cdot 5 + z \cdot x\\ \mathbf{elif}\;x \leq -5 \cdot 10^{-90}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 6.1 \cdot 10^{-33}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -10500000.0)
   (+ (* z x) (* x y))
   (if (<= x -1.56e-52)
     (+ (* z 5.0) (* z x))
     (if (<= x -5e-90)
       (* x y)
       (if (<= x 6.1e-33) (* z (+ 5.0 x)) (* x (+ z y)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -10500000.0) {
		tmp = (z * x) + (x * y);
	} else if (x <= -1.56e-52) {
		tmp = (z * 5.0) + (z * x);
	} else if (x <= -5e-90) {
		tmp = x * y;
	} else if (x <= 6.1e-33) {
		tmp = z * (5.0 + x);
	} else {
		tmp = x * (z + y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-10500000.0d0)) then
        tmp = (z * x) + (x * y)
    else if (x <= (-1.56d-52)) then
        tmp = (z * 5.0d0) + (z * x)
    else if (x <= (-5d-90)) then
        tmp = x * y
    else if (x <= 6.1d-33) then
        tmp = z * (5.0d0 + x)
    else
        tmp = x * (z + y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -10500000.0) {
		tmp = (z * x) + (x * y);
	} else if (x <= -1.56e-52) {
		tmp = (z * 5.0) + (z * x);
	} else if (x <= -5e-90) {
		tmp = x * y;
	} else if (x <= 6.1e-33) {
		tmp = z * (5.0 + x);
	} else {
		tmp = x * (z + y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -10500000.0:
		tmp = (z * x) + (x * y)
	elif x <= -1.56e-52:
		tmp = (z * 5.0) + (z * x)
	elif x <= -5e-90:
		tmp = x * y
	elif x <= 6.1e-33:
		tmp = z * (5.0 + x)
	else:
		tmp = x * (z + y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -10500000.0)
		tmp = Float64(Float64(z * x) + Float64(x * y));
	elseif (x <= -1.56e-52)
		tmp = Float64(Float64(z * 5.0) + Float64(z * x));
	elseif (x <= -5e-90)
		tmp = Float64(x * y);
	elseif (x <= 6.1e-33)
		tmp = Float64(z * Float64(5.0 + x));
	else
		tmp = Float64(x * Float64(z + y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -10500000.0)
		tmp = (z * x) + (x * y);
	elseif (x <= -1.56e-52)
		tmp = (z * 5.0) + (z * x);
	elseif (x <= -5e-90)
		tmp = x * y;
	elseif (x <= 6.1e-33)
		tmp = z * (5.0 + x);
	else
		tmp = x * (z + y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -10500000.0], N[(N[(z * x), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -1.56e-52], N[(N[(z * 5.0), $MachinePrecision] + N[(z * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -5e-90], N[(x * y), $MachinePrecision], If[LessEqual[x, 6.1e-33], N[(z * N[(5.0 + x), $MachinePrecision]), $MachinePrecision], N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if x < -1.05e7

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 99.9%

      \[\leadsto \color{blue}{\left(y + z\right) \cdot x} \]
   3. Step-by-step derivation

      1. +-commutative 99.9%

         \[\leadsto \color{blue}{\left(z + y\right)} \cdot x \]

   4. Simplified 99.9%

      \[\leadsto \color{blue}{\left(z + y\right) \cdot x} \]

   5. Taylor expanded in z around 0 100.0%

      \[\leadsto \color{blue}{z \cdot x + y \cdot x} \]

   if -1.05e7 < x < -1.5600000000000001e-52

   1. Initial program 99.8%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around 0 67.3%

      \[\leadsto \color{blue}{z \cdot x} + z \cdot 5 \]

   if -1.5600000000000001e-52 < x < -5.00000000000000019e-90

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 100.0%

      \[\leadsto \color{blue}{y \cdot x} \]

   if -5.00000000000000019e-90 < x < 6.1000000000000001e-33

   1. Initial program 99.8%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around 0 73.5%

      \[\leadsto \color{blue}{z \cdot x + 5 \cdot z} \]
   3. Step-by-step derivation

      1. +-commutative 73.5%

         \[\leadsto \color{blue}{5 \cdot z + z \cdot x} \]

      2. *-commutative 73.5%

         \[\leadsto 5 \cdot z + \color{blue}{x \cdot z} \]

      3. distribute-rgt-in 73.5%

         \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   4. Simplified 73.5%

      \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   if 6.1000000000000001e-33 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 98.8%

      \[\leadsto \color{blue}{\left(y + z\right) \cdot x} \]
   3. Step-by-step derivation

      1. +-commutative 98.8%

         \[\leadsto \color{blue}{\left(z + y\right)} \cdot x \]

   4. Simplified 98.8%

      \[\leadsto \color{blue}{\left(z + y\right) \cdot x} \]
3. Recombined 5 regimes into one program.

4. Final simplification 85.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -10500000:\\ \;\;\;\;z \cdot x + x \cdot y\\ \mathbf{elif}\;x \leq -1.56 \cdot 10^{-52}:\\ \;\;\;\;z \cdot 5 + z \cdot x\\ \mathbf{elif}\;x \leq -5 \cdot 10^{-90}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 6.1 \cdot 10^{-33}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \end{array} \]

Alternative 5: 97.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5:\\ \;\;\;\;z \cdot x + x \cdot y\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{-28}:\\ \;\;\;\;x \cdot y - z \cdot -5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -5.0)
   (+ (* z x) (* x y))
   (if (<= x 1.75e-28) (- (* x y) (* z -5.0)) (* x (+ z y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -5.0) {
		tmp = (z * x) + (x * y);
	} else if (x <= 1.75e-28) {
		tmp = (x * y) - (z * -5.0);
	} else {
		tmp = x * (z + y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-5.0d0)) then
        tmp = (z * x) + (x * y)
    else if (x <= 1.75d-28) then
        tmp = (x * y) - (z * (-5.0d0))
    else
        tmp = x * (z + y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -5.0) {
		tmp = (z * x) + (x * y);
	} else if (x <= 1.75e-28) {
		tmp = (x * y) - (z * -5.0);
	} else {
		tmp = x * (z + y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -5.0:
		tmp = (z * x) + (x * y)
	elif x <= 1.75e-28:
		tmp = (x * y) - (z * -5.0)
	else:
		tmp = x * (z + y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -5.0)
		tmp = Float64(Float64(z * x) + Float64(x * y));
	elseif (x <= 1.75e-28)
		tmp = Float64(Float64(x * y) - Float64(z * -5.0));
	else
		tmp = Float64(x * Float64(z + y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -5.0)
		tmp = (z * x) + (x * y);
	elseif (x <= 1.75e-28)
		tmp = (x * y) - (z * -5.0);
	else
		tmp = x * (z + y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -5.0], N[(N[(z * x), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.75e-28], N[(N[(x * y), $MachinePrecision] - N[(z * -5.0), $MachinePrecision]), $MachinePrecision], N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -5

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 98.1%

      \[\leadsto \color{blue}{\left(y + z\right) \cdot x} \]
   3. Step-by-step derivation

      1. +-commutative 98.1%

         \[\leadsto \color{blue}{\left(z + y\right)} \cdot x \]

   4. Simplified 98.1%

      \[\leadsto \color{blue}{\left(z + y\right) \cdot x} \]

   5. Taylor expanded in z around 0 98.2%

      \[\leadsto \color{blue}{z \cdot x + y \cdot x} \]

   if -5 < x < 1.75e-28

   1. Initial program 99.8%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]
   2. Step-by-step derivation

      1. +-commutative 99.8%

         \[\leadsto \color{blue}{z \cdot 5 + x \cdot \left(y + z\right)} \]

      2. fma-def 100.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(z, 5, x \cdot \left(y + z\right)\right)} \]

   3. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 5, x \cdot \left(y + z\right)\right)} \]

   4. Taylor expanded in z around -inf 99.8%

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(-1 \cdot x - 5\right)\right) + y \cdot x} \]
   5. Step-by-step derivation

      1. +-commutative 99.8%

         \[\leadsto \color{blue}{y \cdot x + -1 \cdot \left(z \cdot \left(-1 \cdot x - 5\right)\right)} \]

      2. fma-def 99.8%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, x, -1 \cdot \left(z \cdot \left(-1 \cdot x - 5\right)\right)\right)} \]

      3. mul-1-neg 99.8%

         \[\leadsto \mathsf{fma}\left(y, x, \color{blue}{-z \cdot \left(-1 \cdot x - 5\right)}\right) \]

      4. fma-neg 99.8%

         \[\leadsto \color{blue}{y \cdot x - z \cdot \left(-1 \cdot x - 5\right)} \]

      5. sub-neg 99.8%

         \[\leadsto y \cdot x - z \cdot \color{blue}{\left(-1 \cdot x + \left(-5\right)\right)} \]

      6. +-commutative 99.8%

         \[\leadsto y \cdot x - z \cdot \color{blue}{\left(\left(-5\right) + -1 \cdot x\right)} \]

      7. mul-1-neg 99.8%

         \[\leadsto y \cdot x - z \cdot \left(\left(-5\right) + \color{blue}{\left(-x\right)}\right) \]

      8. unsub-neg 99.8%

         \[\leadsto y \cdot x - z \cdot \color{blue}{\left(\left(-5\right) - x\right)} \]

      9. metadata-eval 99.8%

         \[\leadsto y \cdot x - z \cdot \left(\color{blue}{-5} - x\right) \]

   6. Simplified 99.8%

      \[\leadsto \color{blue}{y \cdot x - z \cdot \left(-5 - x\right)} \]

   7. Taylor expanded in x around 0 99.0%

      \[\leadsto y \cdot x - \color{blue}{-5 \cdot z} \]
   8. Step-by-step derivation

      1. *-commutative 99.0%

         \[\leadsto y \cdot x - \color{blue}{z \cdot -5} \]

   9. Simplified 99.0%

      \[\leadsto y \cdot x - \color{blue}{z \cdot -5} \]

   if 1.75e-28 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 98.8%

      \[\leadsto \color{blue}{\left(y + z\right) \cdot x} \]
   3. Step-by-step derivation

      1. +-commutative 98.8%

         \[\leadsto \color{blue}{\left(z + y\right)} \cdot x \]

   4. Simplified 98.8%

      \[\leadsto \color{blue}{\left(z + y\right) \cdot x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5:\\ \;\;\;\;z \cdot x + x \cdot y\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{-28}:\\ \;\;\;\;x \cdot y - z \cdot -5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z + y\right)\\ \end{array} \]

Alternative 6: 75.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+115}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{+54}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.9e+115) (* x y) (if (<= y 5.6e+54) (* z (+ 5.0 x)) (* x y))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.9e+115) {
		tmp = x * y;
	} else if (y <= 5.6e+54) {
		tmp = z * (5.0 + x);
	} else {
		tmp = x * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-1.9d+115)) then
        tmp = x * y
    else if (y <= 5.6d+54) then
        tmp = z * (5.0d0 + x)
    else
        tmp = x * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.9e+115) {
		tmp = x * y;
	} else if (y <= 5.6e+54) {
		tmp = z * (5.0 + x);
	} else {
		tmp = x * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.9e+115:
		tmp = x * y
	elif y <= 5.6e+54:
		tmp = z * (5.0 + x)
	else:
		tmp = x * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.9e+115)
		tmp = Float64(x * y);
	elseif (y <= 5.6e+54)
		tmp = Float64(z * Float64(5.0 + x));
	else
		tmp = Float64(x * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.9e+115)
		tmp = x * y;
	elseif (y <= 5.6e+54)
		tmp = z * (5.0 + x);
	else
		tmp = x * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.9e+115], N[(x * y), $MachinePrecision], If[LessEqual[y, 5.6e+54], N[(z * N[(5.0 + x), $MachinePrecision]), $MachinePrecision], N[(x * y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.9e115 or 5.6000000000000003e54 < y

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 76.1%

      \[\leadsto \color{blue}{y \cdot x} \]

   if -1.9e115 < y < 5.6000000000000003e54

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around 0 78.8%

      \[\leadsto \color{blue}{z \cdot x + 5 \cdot z} \]
   3. Step-by-step derivation

      1. +-commutative 78.8%

         \[\leadsto \color{blue}{5 \cdot z + z \cdot x} \]

      2. *-commutative 78.8%

         \[\leadsto 5 \cdot z + \color{blue}{x \cdot z} \]

      3. distribute-rgt-in 78.7%

         \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   4. Simplified 78.7%

      \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 77.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+115}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{+54}:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \]

Alternative 7: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ z \cdot 5 + x \cdot \left(z + y\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* z 5.0) (* x (+ z y))))

C

double code(double x, double y, double z) {
	return (z * 5.0) + (x * (z + y));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (z * 5.0d0) + (x * (z + y))
end function

Java

public static double code(double x, double y, double z) {
	return (z * 5.0) + (x * (z + y));
}

Python

def code(x, y, z):
	return (z * 5.0) + (x * (z + y))

Julia

function code(x, y, z)
	return Float64(Float64(z * 5.0) + Float64(x * Float64(z + y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (z * 5.0) + (x * (z + y));
end

Wolfram

code[x_, y_, z_] := N[(N[(z * 5.0), $MachinePrecision] + N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]

2. Final simplification 99.9%

   \[\leadsto z \cdot 5 + x \cdot \left(z + y\right) \]

Alternative 8: 61.1% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9.8 \cdot 10^{-85}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 5.6 \cdot 10^{-31}:\\ \;\;\;\;z \cdot 5\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -9.8e-85) (* x y) (if (<= x 5.6e-31) (* z 5.0) (* x y))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -9.8e-85) {
		tmp = x * y;
	} else if (x <= 5.6e-31) {
		tmp = z * 5.0;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-9.8d-85)) then
        tmp = x * y
    else if (x <= 5.6d-31) then
        tmp = z * 5.0d0
    else
        tmp = x * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -9.8e-85) {
		tmp = x * y;
	} else if (x <= 5.6e-31) {
		tmp = z * 5.0;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -9.8e-85:
		tmp = x * y
	elif x <= 5.6e-31:
		tmp = z * 5.0
	else:
		tmp = x * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -9.8e-85)
		tmp = Float64(x * y);
	elseif (x <= 5.6e-31)
		tmp = Float64(z * 5.0);
	else
		tmp = Float64(x * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -9.8e-85)
		tmp = x * y;
	elseif (x <= 5.6e-31)
		tmp = z * 5.0;
	else
		tmp = x * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -9.8e-85], N[(x * y), $MachinePrecision], If[LessEqual[x, 5.6e-31], N[(z * 5.0), $MachinePrecision], N[(x * y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -9.80000000000000029e-85 or 5.5999999999999998e-31 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 53.3%

      \[\leadsto \color{blue}{y \cdot x} \]

   if -9.80000000000000029e-85 < x < 5.5999999999999998e-31

   1. Initial program 99.8%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around 0 73.5%

      \[\leadsto \color{blue}{5 \cdot z} \]
3. Recombined 2 regimes into one program.

4. Final simplification 62.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9.8 \cdot 10^{-85}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 5.6 \cdot 10^{-31}:\\ \;\;\;\;z \cdot 5\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \]

Alternative 9: 61.3% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq 6 \cdot 10^{-35}:\\ \;\;\;\;z \cdot 5\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -5.0) (* z x) (if (<= x 6e-35) (* z 5.0) (* x y))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -5.0) {
		tmp = z * x;
	} else if (x <= 6e-35) {
		tmp = z * 5.0;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-5.0d0)) then
        tmp = z * x
    else if (x <= 6d-35) then
        tmp = z * 5.0d0
    else
        tmp = x * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -5.0) {
		tmp = z * x;
	} else if (x <= 6e-35) {
		tmp = z * 5.0;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -5.0:
		tmp = z * x
	elif x <= 6e-35:
		tmp = z * 5.0
	else:
		tmp = x * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -5.0)
		tmp = Float64(z * x);
	elseif (x <= 6e-35)
		tmp = Float64(z * 5.0);
	else
		tmp = Float64(x * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -5.0)
		tmp = z * x;
	elseif (x <= 6e-35)
		tmp = z * 5.0;
	else
		tmp = x * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -5.0], N[(z * x), $MachinePrecision], If[LessEqual[x, 6e-35], N[(z * 5.0), $MachinePrecision], N[(x * y), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -5

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around 0 67.3%

      \[\leadsto \color{blue}{z \cdot x + 5 \cdot z} \]
   3. Step-by-step derivation

      1. +-commutative 67.3%

         \[\leadsto \color{blue}{5 \cdot z + z \cdot x} \]

      2. *-commutative 67.3%

         \[\leadsto 5 \cdot z + \color{blue}{x \cdot z} \]

      3. distribute-rgt-in 67.3%

         \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   4. Simplified 67.3%

      \[\leadsto \color{blue}{z \cdot \left(5 + x\right)} \]

   5. Taylor expanded in x around inf 65.5%

      \[\leadsto \color{blue}{z \cdot x} \]

   if -5 < x < 5.99999999999999978e-35

   1. Initial program 99.8%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around 0 69.1%

      \[\leadsto \color{blue}{5 \cdot z} \]

   if 5.99999999999999978e-35 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 62.9%

      \[\leadsto \color{blue}{y \cdot x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 66.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq 6 \cdot 10^{-35}:\\ \;\;\;\;z \cdot 5\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \]

Alternative 10: 37.6% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ z \cdot 5 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* z 5.0))

C

double code(double x, double y, double z) {
	return z * 5.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = z * 5.0d0
end function

Java

public static double code(double x, double y, double z) {
	return z * 5.0;
}

Python

def code(x, y, z):
	return z * 5.0

Julia

function code(x, y, z)
	return Float64(z * 5.0)
end

MATLAB

function tmp = code(x, y, z)
	tmp = z * 5.0;
end

Wolfram

code[x_, y_, z_] := N[(z * 5.0), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]

2. Taylor expanded in x around 0 38.6%

   \[\leadsto \color{blue}{5 \cdot z} \]

3. Final simplification 38.6%

   \[\leadsto z \cdot 5 \]

Developer target: 97.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x + 5\right) \cdot z + x \cdot y \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* (+ x 5.0) z) (* x y)))

C

double code(double x, double y, double z) {
	return ((x + 5.0) * z) + (x * y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((x + 5.0d0) * z) + (x * y)
end function

Java

public static double code(double x, double y, double z) {
	return ((x + 5.0) * z) + (x * y);
}

Python

def code(x, y, z):
	return ((x + 5.0) * z) + (x * y)

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x + 5.0) * z) + Float64(x * y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x + 5.0) * z) + (x * y);
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x + 5.0), $MachinePrecision] * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023185 
(FPCore (x y z)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, C"
  :precision binary64

  :herbie-target
  (+ (* (+ x 5.0) z) (* x y))

  (+ (* x (+ y z)) (* z 5.0)))